(1) Field of the Invention
The present invention relates to adaptive equalizers and more particularly to a novel method and apparatus for declaring convergence for an adaptive equalizer.
(2) Description of Related Art including information disclosed under 37 CFR 1.97 and 1.98.
In modern asynchronous communications systems, a data stream is transmitted across a communications channel by a transmitter to a receiver without any ancillary clocking or synchronizing information. In order to properly interpret and process the data stream at the receiver, the data stream itself must be processed to extract an underlying clock signal that can drive the circuitry that will decode and extract the data from the data stream.
In a perfect communications environment, that is, a noiseless communications channel without band-limiting, such clock and data recovery is relatively straightforward. However, typically, the communications channel is relatively noisy, may also be band-limited and effectively distorts the signal conveyed thereby.
This distortion is frequently exhibited as inter-symbol interference (ISI).
Accordingly, as a prelude to the clock and data recovery process, the data stream must be conditioned in order to remove ISI and/or compensate or attenuate the noise in the channel as much as possible and thus maximize the ability of the clock and data recovery circuitry to perform its tasks accurately.
The conditioning step is typically performed by an equalizer. Preferably, the equalizer is an adaptive equalizer that adapts its parameters to the time-varying data stream and effectively minimizes the bit error rate (BER).
Adaptive equalizers are well known in the art. They may be either analog or digital or a combination thereof. As the processing of the data stream proceeds, they typically converge to a steady-state.
Many communications systems are configured so that the data stream initially provides a known training sequence before any data. This provides the equalizer both time to converge and a known bit sequence that will assist in processing.
While the equalizer preferably converges to a situation where it correctly processes the data stream, it is possible to conceivably converge to a situation where it incorrectly processes the received data stream. Such a situation is known in the art as false convergence.
In theory, in order to determine whether or not the convergence of an adaptive equalizer has been successful, one could look at the BER. False convergence would be indicated by a high BER.
Most adaptive equalizers in the art typically do not actually measure the BER to confirm that the adaptive equalizer is functioning properly. This is because hitherto, the calculation of BER demands relatively complex logic. Moreover, the BER is calculable only after the data has been decoded, which usually occurs downstream of the equalization process.
Furthermore, until now there have not been scenarios where the need to minimize BER has called for direct measurement of this metric.
Therefore, inferences about the BER are typically drawn from circumstantial factors, whose parameters are more easily, quickly or conveniently obtained. For example, one popular metric is signal to noise ratio (SNR). Generally, a large SNR is an indication of low BER and the attendant inference that the ISI has been reduced to a tolerable level so that the signal can be properly recovered. One advantage of this indirect metric is that the equalizer can obtain SNR information by monitoring certain aspects of the (often analog) circuitry of the equalizer itself.
This metric is not universally accurate. For example, the adaptive equalizer may have converged however, but to a false location. For example, if the equalizer has converged falsely, it is conceivable that the receiver is in fact inverting the recovered data. In this scenario, the SNR would show perfect performance, but in reality, the BER would be 100%.
In many cases, safeguards may be engineered into the receiver to reduce the likelihood that a measurement of low SNR imparts a false indication of low BER. For example, if the equalizer is relatively simple and has only a few taps, convergence may be fairly straightforward.
Furthermore, often the channel does not severely distort the data so that only minimal equalization is called for. In such situations, data can still be correctly recovered relatively easily, so that adaptive equalization, which is more complicated and thus more likely to falsely converge, is unnecessary.
However, as demand for channel capacity and faster data rates increases, newer communications standards impose more and more rigorous demands that in turn call for more complicated equalizers.
One such standard is IEEE 10G-LRM, which is a part of IEEE 802.3AQ. This standard specifies rules for the transmission of data over a multimode fiber.
The IEEE 10G-LRM standard demands support for very specific types of multimode fiber, each having different ISI impairments. These demands are not limited to the stressed receiver tests section of the standard.
Further, the standard does not make provision for training sequences, so that any equalization is blind. Blind equalization is generally acknowledged as one of the more demanding equalization problems.
All of these demands impose significant stresses on standard-compliant receivers. Generally, stronger equalization is called for in an attempt to satisfy these demands. However, this concomitantly and substantially increases the opportunity for and the likelihood of false convergence, especially when relying upon the conventional low SNR metric.
What is therefore needed is a novel metric for declaring true convergence of a receiver's adaptive equalizer under current and future asynchronous communications standards.